numpy.ma.mask_or(m1, m2, copy=False, shrink=True) [source] Combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Parameters: m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to nomask if all its values are False. Defaults to True. Returns: mask : outp

numpy.polynomial.chebyshev.chebfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Chebyshev series to data. Return the coefficients of a Legendre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) a

numpy.polynomial.laguerre.lagfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Laguerre series to data. Return the coefficients of a Laguerre series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are

numpy.random.standard_gamma(shape, size=None) Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated ?k?) and scale=1. Parameters: shape : float Parameter, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: samples : ndarray or scalar

numpy.ravel(a, order='C') [source] Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input) Parameters: a : array_like Input array. The elements in a are read in the order specified by order, and packed as a 1-D array. order : {?C?,?F?, ?A?, ?K?}, optional The el

iinfo.min Minimum value of given dtype.

MaskedArray.__sub__(other) [source] Subtract other from self, and return a new masked array.

generic.__array_priority__ Array priority.

MaskedArray.__rdiv__ x.__rdiv__(y) <==> y/x

MaskedArray.__rpow__(other) [source] Raise other to the power self, masking the potential NaNs/Infs